pracaon.pl

Python Software Engineer (Production Data & Model Services) | Middle

Wrocław, Lower Silesian Voivodeship, Polska
EPAM
Partner
dziś
Wynagrodzenie do ustalenia
Pełny etat • Hybrydowa • IT i Telekomunikacja

Najważniejsze cechy oferty

  • Min. 3 lata doświadczenia

  • Backend: Java / .NET / Node / Python

  • Zatrudnienie: Umowa o pracę

  • Model hybrydowy - część pracy zdalnie

Opis

We are seeking a Python Software Engineer to join our Production Data & Model Services team. In this role, you will build and operate production-grade Python applications, transform data science prototypes into deployable services and collaborate with platform teams to deliver robust data pipelines and APIs. Responsibilities Build and run production-grade Python applications (APIs and batch jobs) with strong SDLC practices including code reviews, testing, CI/CD, observability and documentation Develop robust data pipelines (batch and near-real-time) reading and writing governed storage with Parquet/columnar formats and approved patterns Transform quant and data science prototypes into deployable packages/services (typed, modular, versioned) Expose scoring and analytics via APIs or scheduled jobs rather than notebook-only deliverables Collaborate with platform teams on Databricks/Spark connectivity Optimize PySpark workloads when needed Ensure release discipline through Git workflows, automated tests and code reviews Requirements 3+ years of strong Python engineering experience including packaging (wheels/pyproject), typing and clean architecture Proficiency in error handling and performance-oriented development Proven production SDLC background with Git workflows, automated tests and CI/CD Expertise in Pandas and NumPy in production pipelines Familiarity with data formats like Parquet and governed data access patterns Experience building and operating APIs/services using FastAPI, Flask or similar frameworks Competency working in governed platform environments such as Databricks or containerized dev platforms Nice to have Skills in scikit-learn for production feature and scoring pipelines, including reproducible transforms and model packaging/versioning Background in PySpark and distributed processing Knowledge of IDE-to-Databricks workflows such as Databricks Connect

Wymagania

  • 3+ years of strong Python engineering experience including packaging (wheels/pyproject), typing and clean architecture

  • Proficiency in error handling and performance-oriented development

  • Proven production SDLC background with Git workflows, automated tests and CI/CD

  • Expertise in Pandas and NumPy in production pipelines

  • Familiarity with data formats like Parquet and governed data access patterns

  • Experience building and operating APIs/services using FastAPI, Flask or similar frameworks

  • Competency working in governed platform environments such as Databricks or containerized dev platforms

Zakres obowiązków

  • Build and run production-grade Python applications (APIs and batch jobs) with strong SDLC practices including code reviews, testing, CI/CD, observability and documentation

  • Develop robust data pipelines (batch and near-real-time) reading and writing governed storage with Parquet/columnar formats and approved patterns

  • Transform quant and data science prototypes into deployable packages/services (typed, modular, versioned)

  • Expose scoring and analytics via APIs or scheduled jobs rather than notebook-only deliverables

  • Collaborate with platform teams on Databricks/Spark connectivity

  • Optimize PySpark workloads when needed

  • Ensure release discipline through Git workflows, automated tests and code reviews

Seniority

  • Middle

Mile widziane

  • Skills in scikit-learn for production feature and scoring pipelines, including reproducible transforms and model packaging/versioning

  • Background in PySpark and distributed processing

  • Knowledge of IDE-to-Databricks workflows such as Databricks Connect

Słowa kluczowe / Umiejętności

Python.Core
APIs and Integration
NumPy
Pandas
Python
Databricks
Scikit-Learn
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